Feature-based Attribution
Feature-Based Attribution is a malware attribution method that identifies the source or family of malicious software by analyzing specific technical or behavioral characteristics extracted from the code, thereby distinguishing it from other attribution approaches that rely on circumstantial or contextual evidence.
48
Publications
3.3K
Citations
192
Authors
85
Institutions
Publications per year
2017–2026
46
Parents
48
On the Tractability of SHAP Explanations
Guy Van den Broeck, Anton Lykov, Maximilian Schleich et al. · Journal of Artificial Intelligence Research · 2022 · 437 citations · Full text
Algorithms to estimate Shapley value feature attributions
Hugh Chen, Ian Covert, Scott Lundberg et al. · Nature Machine Intelligence · 2023 · 416 citations
Shapley value: from cooperative game to explainable artificial intelligence
Meng Li, Hengyang Sun, Yanjun Huang et al. · Autonomous Intelligent Systems · 2024 · 167 citations · Full text
1–5 of 48
192
5
h-index
4
h-index
3
h-index
3
h-index
2
h-index
1–5 of 192
85
University of Washington
Seattle, United States
5 h-index
17 publications
3.3K citations
Technical University of Munich
Munich, Germany
2 h-index
4 publications
103 citations
University of Sheffield
Sheffield, United Kingdom
2 h-index
2 publications
31 citations
ETH Zurich
Zurich, Switzerland
2 h-index
11 publications
408 citations
Tongji University
Shanghai, China
2 h-index
7 publications
782 citations
Freie Universität Berlin
Berlin, Germany
2 h-index
3 publications
157 citations
Institute of Information Engineering
Beijing, China
2 h-index
4 publications
125 citations

Imperial College London
London, United Kingdom
1 h-index
1 publications
20 citations
1–8 of 85
Repository · 12 publications · 1K citations
12
h-index
Conference · 2 publications · 48 citations
2
h-index
Conference · 2 publications · 124 citations
2
h-index
1 publications · 15 citations
1
h-index
Conference · 1 publications · 46 citations
1
h-index
1–5